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Book (stand-alone)Land Cover Atlas of the Republic of South Sudan 2023
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No results found.Understanding the distribution of different land cover classes, as revealed by the updated Land Cover Atlas, holds paramount importance and is an innovative approach in helping to understand land cover dynamics. It enables decision-makers to harness this knowledge for strategic planning and informed decision-making in sectors such as agriculture, conservation, water resource management, and land degradation prevention. By recognizing the distribution and dynamics of land cover, stakeholders can work towards sustainable development goals, ecological resilience, and improved livelihoods in South Sudan. The development and utilization of the Land Cover Atlas highlight the significance of innovation, collaboration, and partnerships in decision-making processes and land cover management. By fostering collaborative efforts between organizations like the Food and Agriculture Organization of the United Nations (FAO), the Government of South Sudan, and supportive donors, a comprehensive understanding of land dynamics can be achieved. This collaborative approach enables stakeholders to work together towards effective resource management, resilience-building, and sustainable development, benefiting the people and environment of South Sudan. -
Book (stand-alone)Land Cover Atlas of Yemen 2024
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No results found.Understanding the utilization, distribution, temporal variations, and human activities related to natural resources is crucial for sustainable land management, especially in Yemen, a country grappling with prolonged conflicts and severe environmental challenges. The competition for natural resources such as water, arable land, and wood among various stakeholders with diverse interests often leads to land degradation and is a key driver of tensions and conflicts. Therefore, obtaining this fundamental information is imperative for promoting sustainable land use and mitigating the negative impacts of resource competition.In Yemen, land cover mapping is essential to support growing concerns about food and nutrition security, improving the resilience of livelihoods to threats and crises in the context of climate change. This atlas contains the land cover dataset for Yemen, prepared as part of a portfolio of projects aimed at enhancing governance and preventing conflicts across the country. The ultimate goal is to reduce displacement and irregular migration by promoting household food security, nutrition, and income.Creating an accurate initial inventory of natural resources is critical for sustaining these achievements over time. The new land cover dataset allows for detailed mapping of natural resources, human settlements, and activities in Yemen. It represents an updated dataset developed for Yemen, integrating high-resolution multi-temporal imagery, machine-learning algorithms, and the Land Cover Meta Language (LCML) to support the Natural Resource Management strategy, land use planning, and other innovative approaches. -
Book (stand-alone)Land Cover Atlas of the Republic of South Sudan 2011
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No results found.The Land Cover Atlas of the Republic of South Sudan provides information on the land cover distribution by administrative and sub-basin divisions. The dataset was created using the FAO/GLCN methodology and tools. Main data sources include satellite imagery from SPOT and Global Land Survey (GLS) Landsat, existing Africover land cover database and ancillary data. The legend was prepared using the Land Cover Classification System (LCCS): a comprehensive, standardized a priori classification system, designed to meet specific user requirements and created for mapping exercises, independent of the scale or means used to map. The classification uses a set of independent diagnostic criteria that allows the correlation with existing classifications and legends. Satellite images of South Sudan were segmented into homogeneous polygons and they were interpreted according to the FAO/GLCN methodology for the production of a seamless and detailed land cover dataset for the whole country. A field veri fication was completed by national experts who received a customized training on methodology and tools. The final land cover product has around 100,000 polygons, classified into 43 different classes and eventually aggregated into 7 major classes for ease of analysis and display.
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